Cancer immunotherapies importance as an integral standard of care across oncological indications continues to grow. Antibody inhibition of CTLA-4 and PD-1 enhances the antitumor immune response (1), yielding high rates of objective clinical responses and ultimately melanoma and lung cancer FDA approvals. A rising challenge for these therapies is the resistance to treatment in a subset of patients due to acquired or intrinsic mechanisms (2). Beyond mutations in the tumor cells themselves, the tumor microenvironment can play an important role in the response to these treatments. It was recently shown that when treating melanoma patients with Ipilimumab, myeloid derived suppressor cells (MDSC) infiltrate into tumor cells of resistant patients and could be a predictive biomarker for resistance(3).
We will be using MetaCore and the Data Annotation & Processing tool to calculate the differentially expressed genes in a publicly available microarray dataset and upload the results into MetaCore for analysis. The data used in this session was reported in the Gene Expression Omnibus (GEO) dataset GSE41620 which studied MDSCs taken from naïve mouse blood and from mice injected with the lymphoma RMA-S cell line. Samples were drawn from blood and tumors of the xenograph and naïve mice. Using this data Pathway Map Creator in MetaCore to answer these questions:
• How to calculate differentially expressed genes from a GEO dataset and upload this data into Metacore?
• What pathway maps are potentially disrupted by the differentially expressed genes?
• What transcription factors could be regulating a significant number of the genes?